Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide
It helps new researchers in medical imaging overcome initial challenges, but it is incremental as it synthesizes existing knowledge into a guide.
This tutorial paper addresses the challenge of medical image segmentation for beginners by providing an overview of fundamental concepts, deep learning algorithms, tools, and best practices, with sample tasks and code available on GitHub.
Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis. However, many researchers who are new to the field struggle with basic concepts. This tutorial paper aims to provide an overview of the fundamental concepts of medical imaging, with a focus on Magnetic Resonance and Computerized Tomography. We will also discuss deep learning algorithms, tools, and frameworks used for segmentation tasks, and suggest best practices for method development and image analysis. Our tutorial includes sample tasks using public data, and accompanying code is available on GitHub (https://github.com/MICLab-Unicamp/Medical-ImagingTutorial). By sharing our insights gained from years of experience in the field and learning from relevant literature, we hope to assist researchers in overcoming the initial challenges they may encounter in this exciting and important area of research.